A Study on the Development of a Big Data-Based Safety Management Platform for uninterruptible Diagnosis and Real-Time Risk Prediction of Electrical Facilities

This study aims to redefine the value and management approach of private customers’ electrical equipment assets by enabling safer and more efficient maintenance and replacement practices. To address the limitations of conventional methods in detecting early signs of equipment abnormalities, we introduce real-time diagnostic technologies that enhance the reliability and responsiveness of condition assessment. The proposed approach integrates IoT-based sensing and AI-driven anomaly detection to improve the accuracy of equipment monitoring and support continuous, non-interruptive operation. Furthermore, based on these technologies, we seek to develop a big-data standard platform that can contribute to broader public safety by enabling systematic data collection, predictive maintenance, and efficient safety management. Ultimately, the outcomes of this research are expected to strengthen preventive maintenance capabilities and enhance the overall safety and operational reliability of electrical equipment.